{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "tutorial.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "V8-yl-s-WKMG"
      },
      "source": [
        "# EfficientDet Tutorial: inference, eval, and training \n",
        "\n",
        "\n",
        "\n",
        "<table align=\"left\"><td>\n",
        "  <a target=\"_blank\"  href=\"https://github.com/google/automl/blob/master/efficientdet/tutorial.ipynb\">\n",
        "    <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on github\n",
        "  </a>\n",
        "</td><td>\n",
        "  <a target=\"_blank\"  href=\"https://colab.sandbox.google.com/github/google/automl/blob/master/efficientdet/tutorial.ipynb\">\n",
        "    <img width=32px src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "</td></table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "muwOCNHaq85j",
        "colab_type": "text"
      },
      "source": [
        "# 0. Install and view graph."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dggLVarNxxvC"
      },
      "source": [
        "## 0.1 Install package and download source code/image.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hGL97-GXjSUw",
        "colab": {}
      },
      "source": [
        "%%capture\n",
        "#@title\n",
        "# Install tensorflow and pycocotools\n",
        "!pip install tensorflow\n",
        "!pip install pytype\n",
        "# The default pycocotools doesn't work for python3: https://github.com/cocodataset/cocoapi/issues/49\n",
        "!pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'\n",
        "\n",
        "import os\n",
        "import sys\n",
        "import tensorflow.compat.v1 as tf\n",
        "\n",
        "# Download source code.\n",
        "if \"efficientdet\" not in os.getcwd():\n",
        "  !git clone --depth 1 https://github.com/google/automl\n",
        "  os.chdir('automl/efficientdet')\n",
        "  sys.path.append('.')\n",
        "else:\n",
        "  !git pull"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tow-ic7H3d7i",
        "colab_type": "code",
        "outputId": "be86cdc5-682d-4aff-b9f4-04d836ba8c1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "MODEL = 'efficientdet-d0'  #@param\n",
        "\n",
        "def download(m):\n",
        "  if m not in os.listdir():\n",
        "    !wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/{m}.tar.gz\n",
        "    !tar zxf {m}.tar.gz\n",
        "  ckpt_path = os.path.join(os.getcwd(), m)\n",
        "  return ckpt_path\n",
        "\n",
        "# Download checkpoint.\n",
        "ckpt_path = download(MODEL)\n",
        "print('Use model in {}'.format(ckpt_path))\n",
        "\n",
        "# Prepare image and visualization settings.\n",
        "image_url =  'https://user-images.githubusercontent.com/11736571/77320690-099af300-6d37-11ea-9d86-24f14dc2d540.png'#@param\n",
        "image_name = 'img.png' #@param\n",
        "!wget {image_url} -O img.png\n",
        "import os\n",
        "img_path = os.path.join(os.getcwd(), 'img.png')\n",
        "\n",
        "min_score_thresh = 0.4  #@param\n",
        "max_boxes_to_draw = 200  #@param\n",
        "line_thickness =   2#@param\n",
        "\n",
        "import PIL\n",
        "# Get the largest of height/width and round to 128.\n",
        "image_size = max(PIL.Image.open(img_path).size)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Use model in /content/automl/efficientdet/efficientdet-d0\n",
            "--2020-04-23 04:47:47--  https://user-images.githubusercontent.com/11736571/77320690-099af300-6d37-11ea-9d86-24f14dc2d540.png\n",
            "Resolving user-images.githubusercontent.com (user-images.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to user-images.githubusercontent.com (user-images.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4080549 (3.9M) [image/png]\n",
            "Saving to: ‘img.png’\n",
            "\n",
            "img.png             100%[===================>]   3.89M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2020-04-23 04:47:47 (28.1 MB/s) - ‘img.png’ saved [4080549/4080549]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GvdjcYpUVuQ5",
        "colab_type": "text"
      },
      "source": [
        "## 0.2 View graph in TensorBoard"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2oz3r1LUDzr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!python model_inspect.py --model_name={MODEL} --logdir=logs &> /dev/null\n",
        "%load_ext tensorboard\n",
        "%tensorboard --logdir logs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vZk2dwOxrGhY",
        "colab_type": "text"
      },
      "source": [
        "# 1. inference"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_VaF_j7jdVCK",
        "colab_type": "text"
      },
      "source": [
        "## 1.1 Benchmark networrk latency\n",
        "There are two types of latency:\n",
        "network latency and end-to-end latency.\n",
        "\n",
        "*   network latency: from the first conv op to the network class and box prediction.\n",
        "*   end-to-end latency: from image preprocessing, network, to the final postprocessing to generate a annotated new image.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_3gL01UbDLH",
        "colab_type": "code",
        "outputId": "7fcf42cf-e751-4ec3-b33b-41ce4f36898c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# benchmaak network latency\n",
        "!python model_inspect.py --runmode=bm --model_name=efficientdet-d3\n",
        "\n",
        "# With colab + Tesla T4 GPU, here are the batch size 1 latency summary:\n",
        "# D0:  14.9ms,  FPS = 67.2   (batch size 8 FPS=92.8)\n",
        "# D1:  29.0ms,  FPS = 34.4   (batch size 8 FPS=41.6)\n",
        "# D2:  43.2ms,  FPS = 23.1   (batch size 8 FPS=27.2)\n",
        "# D3:  76.7ms,  FPS = 13.0   (batch size 8 FPS=14.4)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-04-23 04:57:20.357746: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "non-resource variables are not supported in the long term\n",
            "2020-04-23 04:57:22.571519: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F\n",
            "2020-04-23 04:57:22.576765: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2000175000 Hz\n",
            "2020-04-23 04:57:22.576954: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x176f100 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 04:57:22.576983: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-04-23 04:57:22.578914: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-04-23 04:57:22.713785: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:57:22.714447: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x176f2c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 04:57:22.714480: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2020-04-23 04:57:22.714648: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:57:22.715188: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2020-04-23 04:57:22.715232: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 04:57:22.716717: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-04-23 04:57:22.718280: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-04-23 04:57:22.718599: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-04-23 04:57:22.720153: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-04-23 04:57:22.720905: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-04-23 04:57:22.723825: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 04:57:22.723927: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:57:22.724554: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:57:22.725080: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n",
            "2020-04-23 04:57:22.725128: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 04:57:23.253933: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-04-23 04:57:23.253995: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 \n",
            "2020-04-23 04:57:23.254009: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N \n",
            "2020-04-23 04:57:23.254263: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:57:23.254930: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:57:23.255522: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-04-23 04:57:23.255567: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13970 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "W0423 04:57:25.237747 140216340629376 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "W0423 04:57:29.747181 140216340629376 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "Parsing Inputs...\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:121: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.Conv2D` instead.\n",
            "W0423 04:57:30.190577 140216340629376 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:121: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.Conv2D` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "W0423 04:57:30.194706 140216340629376 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:147: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.MaxPooling2D instead.\n",
            "W0423 04:57:30.235587 140216340629376 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:147: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.MaxPooling2D instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:646: separable_conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.SeparableConv2D` instead.\n",
            "W0423 04:57:30.255567 140216340629376 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:646: separable_conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.SeparableConv2D` instead.\n",
            "Parsing Inputs...\n",
            "Parsing Inputs...\n",
            "Parsing Inputs...\n",
            "backbone+fpn+box params/flops = 12.032296M, 24.890171439B\n",
            "2020-04-23 04:57:40.186517: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 04:57:42.013417: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "Warm up: 0 5.6139s\n",
            "Warm up: 1 0.0782s\n",
            "Warm up: 2 0.0776s\n",
            "Warm up: 3 0.0776s\n",
            "Warm up: 4 0.0782s\n",
            "Start benchmark runs total=10\n",
            "Per batch inference time:  0.07735076949998074\n",
            "FPS:  12.92811960972475\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VW95IodKovEu",
        "colab_type": "text"
      },
      "source": [
        "## 1.2 Benchmark end-to-end latency"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NSf6SrZcdavN",
        "colab_type": "code",
        "outputId": "e4ce89f2-8a0c-43ba-8e06-364dc3ca44f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Benchmark end-to-end latency (: preprocess + network + posprocess).\n",
        "#\n",
        "# With colab + Tesla T4 GPU, here are the batch size 1 latency summary:\n",
        "# D0 (AP=33.5): 23.8ms,  FPS = 42.1   (batch size 4, FPS=79.3)\n",
        "# D1 (AP=39.6): 36.1ms,  FPS = 27.7   (batch size 4, FPS=39.1)\n",
        "# D2 (AP=43.0): 50.7ms,  FPS = 19.7   (batch size 4, FPS=26.0)\n",
        "# D3 (AP=45.8): 84.6ms,  FPS = 11.8   (batch size 4, FPS=13.3)\n",
        "# D4 (AP=49.4): 140ms,   FPS =  7.1   (batch size 4, FPS=7.5)\n",
        "# D5 (AP=50.7): 298ms,   FPS =  3.6\n",
        "# D6 (AP=51.7): 386ms,   FPS =  2.6\n",
        "\n",
        "m = 'efficientdet-d0'  # @param\n",
        "batch_size =   1# @param\n",
        "m_path = download(m)\n",
        "\n",
        "saved_model_dir = 'savedmodel'\n",
        "!rm -rf {saved_model_dir}\n",
        "!python model_inspect.py --runmode=saved_model --model_name={m} \\\n",
        "  --ckpt_path={m_path} --saved_model_dir={saved_model_dir} \\\n",
        "  --batch_size={batch_size}\n",
        "!python model_inspect.py --runmode=saved_model_benchmark --model_name={m} \\\n",
        "  --ckpt_path={m_path} --saved_model_dir={saved_model_dir} \\\n",
        "  --batch_size={batch_size}  --input_image=testdata/img1.jpg\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-04-23 05:31:39.074754: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "non-resource variables are not supported in the long term\n",
            "2020-04-23 05:31:41.445144: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F\n",
            "2020-04-23 05:31:41.450545: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2000175000 Hz\n",
            "2020-04-23 05:31:41.450729: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3047100 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 05:31:41.450758: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-04-23 05:31:41.452797: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-04-23 05:31:41.598244: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 05:31:41.598994: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x30472c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 05:31:41.599024: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2020-04-23 05:31:41.599223: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 05:31:41.599949: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2020-04-23 05:31:41.600029: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 05:31:41.601564: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-04-23 05:31:41.603141: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-04-23 05:31:41.603471: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-04-23 05:31:41.604894: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-04-23 05:31:41.605586: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-04-23 05:31:41.608658: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 05:31:41.608772: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 05:31:41.609392: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 05:31:41.609901: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n",
            "2020-04-23 05:31:41.609950: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 05:31:42.165950: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-04-23 05:31:42.166007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 \n",
            "2020-04-23 05:31:42.166026: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N \n",
            "2020-04-23 05:31:42.166263: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 05:31:42.166892: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 05:31:42.167479: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-04-23 05:31:42.167526: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13970 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/dataloader.py:105: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0423 05:31:42.224150 140468344285056 deprecation.py:323] From /content/automl/efficientdet/dataloader.py:105: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/dataloader.py:108: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0423 05:31:42.228210 140468344285056 deprecation.py:323] From /content/automl/efficientdet/dataloader.py:108: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "W0423 05:31:44.014239 140468344285056 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "W0423 05:31:47.392128 140468344285056 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "3 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "Incomplete shape.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:121: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.Conv2D` instead.\n",
            "W0423 05:31:47.629284 140468344285056 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:121: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.Conv2D` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "W0423 05:31:47.632992 140468344285056 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:147: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.MaxPooling2D instead.\n",
            "W0423 05:31:47.672451 140468344285056 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:147: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.MaxPooling2D instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:646: separable_conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.SeparableConv2D` instead.\n",
            "W0423 05:31:47.694163 140468344285056 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:646: separable_conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.SeparableConv2D` instead.\n",
            "3 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "Incomplete shape.\n",
            "3 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "Incomplete shape.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/moving_averages.py:444: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "W0423 05:31:52.396098 140468344285056 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/moving_averages.py:444: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
            "W0423 05:32:05.051297 140468344285056 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/inference.py:543: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
            "W0423 05:32:10.453881 140468344285056 deprecation.py:323] From /content/automl/efficientdet/inference.py:543: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/graph_util_impl.py:359: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
            "W0423 05:32:10.454175 140468344285056 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/graph_util_impl.py:359: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
            "2020-04-23 05:32:13.544840: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "non-resource variables are not supported in the long term\n",
            "2020-04-23 05:32:15.864159: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F\n",
            "2020-04-23 05:32:15.869641: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2000175000 Hz\n",
            "2020-04-23 05:32:15.869833: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2955100 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 05:32:15.869864: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-04-23 05:32:15.871752: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-04-23 05:32:16.012221: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 05:32:16.012971: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x29552c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 05:32:16.013003: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2020-04-23 05:32:16.013217: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 05:32:16.013908: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2020-04-23 05:32:16.013988: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 05:32:16.015507: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-04-23 05:32:16.017478: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-04-23 05:32:16.017930: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-04-23 05:32:16.021122: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-04-23 05:32:16.021843: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-04-23 05:32:16.025782: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 05:32:16.025919: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 05:32:16.026847: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 05:32:16.027695: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n",
            "2020-04-23 05:32:16.027753: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 05:32:16.572800: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-04-23 05:32:16.572885: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 \n",
            "2020-04-23 05:32:16.572899: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N \n",
            "2020-04-23 05:32:16.573134: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 05:32:16.573807: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 05:32:16.574366: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-04-23 05:32:16.574422: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13970 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/inference.py:537: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.\n",
            "W0423 05:32:16.575351 140041920448384 deprecation.py:323] From /content/automl/efficientdet/inference.py:537: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.\n",
            "2020-04-23 05:32:22.393716: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 05:32:24.156634: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "Per batch inference time:  0.023757550899972557\n",
            "FPS:  42.0918807755203\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jGKs3w2_ZXnu",
        "colab_type": "text"
      },
      "source": [
        "## 1.3 Inference images.\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tlh_S6M9ahe5",
        "colab_type": "code",
        "outputId": "420baca8-7863-41cb-ed78-7aca8b9e5919",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# first export a saved model.\n",
        "saved_model_dir = 'savedmodel'\n",
        "!rm -rf {saved_model_dir}\n",
        "!python model_inspect.py --runmode=saved_model --model_name={MODEL} \\\n",
        "  --ckpt_path={ckpt_path} --saved_model_dir={saved_model_dir}\n",
        "\n",
        "# Then run saved_model_infer to do inference.\n",
        "# Notably: batch_size, image_size must be the same as when it is exported.\n",
        "serve_image_out = 'serve_image_out'\n",
        "!mkdir {serve_image_out}\n",
        "\n",
        "!python model_inspect.py --runmode=saved_model_infer \\\n",
        "  --saved_model_dir={saved_model_dir} \\\n",
        "  --model_name={MODEL}  --input_image=testdata/img1.jpg  \\\n",
        "  --output_image_dir={serve_image_out}"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-04-23 06:11:04.560938: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "non-resource variables are not supported in the long term\n",
            "2020-04-23 06:11:06.929524: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F\n",
            "2020-04-23 06:11:06.935169: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2000175000 Hz\n",
            "2020-04-23 06:11:06.935360: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x23cd100 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 06:11:06.935391: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-04-23 06:11:06.937439: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-04-23 06:11:07.082015: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 06:11:07.082814: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x23cd2c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 06:11:07.082849: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2020-04-23 06:11:07.083076: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 06:11:07.083648: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2020-04-23 06:11:07.083704: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 06:11:07.085343: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-04-23 06:11:07.086957: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-04-23 06:11:07.087329: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-04-23 06:11:07.088883: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-04-23 06:11:07.089664: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-04-23 06:11:07.092796: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 06:11:07.092928: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 06:11:07.093579: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 06:11:07.094133: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n",
            "2020-04-23 06:11:07.094185: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 06:11:07.678635: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-04-23 06:11:07.678698: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 \n",
            "2020-04-23 06:11:07.678713: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N \n",
            "2020-04-23 06:11:07.678946: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 06:11:07.679629: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 06:11:07.680196: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-04-23 06:11:07.680248: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13970 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/dataloader.py:105: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0423 06:11:07.738111 140308235298688 deprecation.py:323] From /content/automl/efficientdet/dataloader.py:105: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/dataloader.py:108: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0423 06:11:07.742325 140308235298688 deprecation.py:323] From /content/automl/efficientdet/dataloader.py:108: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "W0423 06:11:09.586266 140308235298688 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "W0423 06:11:13.001640 140308235298688 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "3 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "Incomplete shape.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:121: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.Conv2D` instead.\n",
            "W0423 06:11:13.238692 140308235298688 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:121: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.Conv2D` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "W0423 06:11:13.242339 140308235298688 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:147: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.MaxPooling2D instead.\n",
            "W0423 06:11:13.290771 140308235298688 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:147: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.MaxPooling2D instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:646: separable_conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.SeparableConv2D` instead.\n",
            "W0423 06:11:13.317819 140308235298688 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:646: separable_conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.SeparableConv2D` instead.\n",
            "3 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "Incomplete shape.\n",
            "3 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "Incomplete shape.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/moving_averages.py:444: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "W0423 06:11:18.193500 140308235298688 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/moving_averages.py:444: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
            "W0423 06:11:30.577863 140308235298688 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/inference.py:543: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
            "W0423 06:11:36.108432 140308235298688 deprecation.py:323] From /content/automl/efficientdet/inference.py:543: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/graph_util_impl.py:359: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
            "W0423 06:11:36.108747 140308235298688 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/graph_util_impl.py:359: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
            "mkdir: cannot create directory ‘serve_image_out’: File exists\n",
            "2020-04-23 06:11:44.974866: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "non-resource variables are not supported in the long term\n",
            "2020-04-23 06:11:47.379789: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F\n",
            "2020-04-23 06:11:47.385505: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2000175000 Hz\n",
            "2020-04-23 06:11:47.385708: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1353100 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 06:11:47.385741: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-04-23 06:11:47.387711: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-04-23 06:11:47.530137: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 06:11:47.530899: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x13532c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 06:11:47.530934: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2020-04-23 06:11:47.531166: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 06:11:47.531759: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2020-04-23 06:11:47.531822: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 06:11:47.533385: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-04-23 06:11:47.534942: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-04-23 06:11:47.535346: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-04-23 06:11:47.536890: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-04-23 06:11:47.537691: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-04-23 06:11:47.540600: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 06:11:47.540726: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 06:11:47.541348: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 06:11:47.541893: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n",
            "2020-04-23 06:11:47.541945: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 06:11:48.110638: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-04-23 06:11:48.110700: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 \n",
            "2020-04-23 06:11:48.110714: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N \n",
            "2020-04-23 06:11:48.110939: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 06:11:48.111662: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 06:11:48.112261: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-04-23 06:11:48.112306: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13970 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/inference.py:537: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.\n",
            "W0423 06:11:48.113143 140461626697600 deprecation.py:323] From /content/automl/efficientdet/inference.py:537: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.\n",
            "2020-04-23 06:11:54.075422: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 06:11:55.899303: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1q2x8s8GpUJz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from IPython import display\n",
        "display.display(display.Image(os.path.join(serve_image_out, '0.jpg')))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fHU46tfckaZo",
        "colab_type": "code",
        "outputId": "415aebc8-5a9b-40f4-a074-b847f14982ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# In case you need to specify different image size or batch size or #boxes, then\n",
        "# you need to export a new saved model and run the inferernce.\n",
        "\n",
        "serve_image_out = 'serve_image_out'\n",
        "!mkdir {serve_image_out}\n",
        "saved_model_dir = 'savedmodel'\n",
        "!rm -rf {saved_model_dir}\n",
        "\n",
        "# Step 1: export model\n",
        "!python model_inspect.py --runmode=saved_model \\\n",
        "  --model_name=efficientdet-d0 --ckpt_path=efficientdet-d0 \\\n",
        "  --input_image_size=1920x1280 --saved_model_dir={saved_model_dir} \\\n",
        "\n",
        "# Step 2: do inference with saved model.\n",
        "!python model_inspect.py --runmode=saved_model_infer \\\n",
        "  --model_name=efficientdet-d0   --ckpt_path=efficientdet-d0 \\\n",
        "  --input_image_size=1920x1280  --saved_model_dir={saved_model_dir} \\\n",
        "  --input_image=img.png --output_image_dir={serve_image_out}\n",
        "\n",
        "from IPython import display\n",
        "display.display(display.Image(os.path.join(serve_image_out, '0.jpg')))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘serve_image_out’: File exists\n",
            "2020-04-23 01:10:37.470291: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "non-resource variables are not supported in the long term\n",
            "2020-04-23 01:10:39.990636: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2300000000 Hz\n",
            "2020-04-23 01:10:39.990893: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2607100 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 01:10:39.990931: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-04-23 01:10:39.993014: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-04-23 01:10:40.142491: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 01:10:40.143266: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x26072c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 01:10:40.143317: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2020-04-23 01:10:40.143543: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 01:10:40.144198: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2020-04-23 01:10:40.144257: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 01:10:40.145858: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-04-23 01:10:40.147357: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-04-23 01:10:40.147750: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-04-23 01:10:40.149194: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-04-23 01:10:40.149972: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-04-23 01:10:40.153183: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 01:10:40.153355: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 01:10:40.154092: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 01:10:40.154679: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n",
            "2020-04-23 01:10:40.154735: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 01:10:40.724933: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-04-23 01:10:40.724995: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 \n",
            "2020-04-23 01:10:40.725010: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N \n",
            "2020-04-23 01:10:40.725277: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 01:10:40.725986: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 01:10:40.726556: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-04-23 01:10:40.726602: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13970 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/dataloader.py:105: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0423 01:10:40.783100 140693133936512 deprecation.py:323] From /content/automl/efficientdet/dataloader.py:105: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/dataloader.py:108: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0423 01:10:40.787201 140693133936512 deprecation.py:323] From /content/automl/efficientdet/dataloader.py:108: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "W0423 01:10:42.716533 140693133936512 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "W0423 01:10:46.261430 140693133936512 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "3 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "Incomplete shape.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:121: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.Conv2D` instead.\n",
            "W0423 01:10:46.513269 140693133936512 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:121: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.Conv2D` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "W0423 01:10:46.516823 140693133936512 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:147: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.MaxPooling2D instead.\n",
            "W0423 01:10:46.561478 140693133936512 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:147: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.MaxPooling2D instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:646: separable_conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.SeparableConv2D` instead.\n",
            "W0423 01:10:46.584539 140693133936512 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:646: separable_conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.SeparableConv2D` instead.\n",
            "3 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "Incomplete shape.\n",
            "3 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "Incomplete shape.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/moving_averages.py:444: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "W0423 01:10:51.954726 140693133936512 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/moving_averages.py:444: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
            "W0423 01:11:05.404613 140693133936512 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/inference.py:543: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
            "W0423 01:11:11.595431 140693133936512 deprecation.py:323] From /content/automl/efficientdet/inference.py:543: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/graph_util_impl.py:359: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
            "W0423 01:11:11.595748 140693133936512 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/graph_util_impl.py:359: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
            "2020-04-23 01:11:14.894774: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "non-resource variables are not supported in the long term\n",
            "2020-04-23 01:11:17.373884: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2300000000 Hz\n",
            "2020-04-23 01:11:17.374101: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x27ef100 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 01:11:17.374137: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-04-23 01:11:17.376243: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-04-23 01:11:17.526397: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 01:11:17.527194: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x27ef2c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 01:11:17.527231: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2020-04-23 01:11:17.527458: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 01:11:17.528083: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2020-04-23 01:11:17.528142: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 01:11:17.529722: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-04-23 01:11:17.531275: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-04-23 01:11:17.531625: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-04-23 01:11:17.533167: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-04-23 01:11:17.533940: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-04-23 01:11:17.537010: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 01:11:17.537185: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 01:11:17.537882: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 01:11:17.538442: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n",
            "2020-04-23 01:11:17.538494: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 01:11:18.122996: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-04-23 01:11:18.123074: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 \n",
            "2020-04-23 01:11:18.123092: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N \n",
            "2020-04-23 01:11:18.123371: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 01:11:18.124056: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 01:11:18.124632: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-04-23 01:11:18.124708: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13970 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/inference.py:537: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.\n",
            "W0423 01:11:18.125554 139700281091968 deprecation.py:323] From /content/automl/efficientdet/inference.py:537: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.\n",
            "2020-04-23 01:11:24.583212: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 01:11:26.605766: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vxm-kvfuAZne",
        "colab_type": "text"
      },
      "source": [
        "## 1.4 Inference video"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Pdnd1kQAgKY",
        "colab_type": "code",
        "outputId": "acb01fa7-c2da-4f6f-efa3-273ef1371fd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# step 0: download video\n",
        "video_url = 'https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/data/video480p.mov'  # @param\n",
        "!wget {video_url} -O input.mov\n",
        "\n",
        "# Step 1: export model\n",
        "saved_model_dir = 'savedmodel'\n",
        "!rm -rf {saved_model_dir}\n",
        "\n",
        "!python model_inspect.py --runmode=saved_model \\\n",
        "  --model_name=efficientdet-d0 --ckpt_path=efficientdet-d0 \\\n",
        "  --saved_model_dir={saved_model_dir}\n",
        "\n",
        "# Step 2: do inference with saved model using saved_model_video\n",
        "!python model_inspect.py --runmode=saved_model_video \\\n",
        "  --model_name=efficientdet-d0   --ckpt_path=efficientdet-d0 \\\n",
        "  --saved_model_dir={saved_model_dir} \\\n",
        "  --input_video=input.mov --output_video=output.mov\n",
        "# Then you can view the output.mov"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-04-23 03:48:16--  https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/data/video480p.mov\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.128.128, 2a00:1450:4013:c02::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.128.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 18511760 (18M) [application/octet-stream]\n",
            "Saving to: ‘input.mov’\n",
            "\n",
            "\rinput.mov             0%[                    ]       0  --.-KB/s               \rinput.mov           100%[===================>]  17.65M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2020-04-23 03:48:16 (146 MB/s) - ‘input.mov’ saved [18511760/18511760]\n",
            "\n",
            "2020-04-23 03:48:18.987106: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "non-resource variables are not supported in the long term\n",
            "2020-04-23 03:48:21.066018: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F\n",
            "2020-04-23 03:48:21.071180: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2000170000 Hz\n",
            "2020-04-23 03:48:21.071403: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3065100 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 03:48:21.071431: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-04-23 03:48:21.073254: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-04-23 03:48:21.203657: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 03:48:21.204289: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x30652c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 03:48:21.204322: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2020-04-23 03:48:21.204497: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 03:48:21.205011: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2020-04-23 03:48:21.205063: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 03:48:21.206513: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-04-23 03:48:21.208037: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-04-23 03:48:21.208393: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-04-23 03:48:21.209804: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-04-23 03:48:21.210478: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-04-23 03:48:21.213292: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 03:48:21.213404: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 03:48:21.213933: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 03:48:21.214417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n",
            "2020-04-23 03:48:21.214461: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 03:48:21.728982: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-04-23 03:48:21.729037: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 \n",
            "2020-04-23 03:48:21.729049: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N \n",
            "2020-04-23 03:48:21.729257: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 03:48:21.729824: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 03:48:21.730333: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-04-23 03:48:21.730375: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13970 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/dataloader.py:105: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0423 03:48:21.777964 140308201023360 deprecation.py:323] From /content/automl/efficientdet/dataloader.py:105: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/dataloader.py:108: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0423 03:48:21.781429 140308201023360 deprecation.py:323] From /content/automl/efficientdet/dataloader.py:108: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "W0423 03:48:23.387426 140308201023360 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "W0423 03:48:26.329375 140308201023360 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "3 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "Incomplete shape.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:121: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.Conv2D` instead.\n",
            "W0423 03:48:26.551935 140308201023360 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:121: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.Conv2D` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "W0423 03:48:26.557302 140308201023360 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:147: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.MaxPooling2D instead.\n",
            "W0423 03:48:26.612656 140308201023360 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:147: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.MaxPooling2D instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:646: separable_conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.SeparableConv2D` instead.\n",
            "W0423 03:48:26.643741 140308201023360 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:646: separable_conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.SeparableConv2D` instead.\n",
            "3 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "Incomplete shape.\n",
            "3 ops no flops stats due to incomplete shapes.\n",
            "Parsing Inputs...\n",
            "Incomplete shape.\n",
            "Incomplete shape.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/moving_averages.py:444: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "W0423 03:48:30.924926 140308201023360 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/moving_averages.py:444: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
            "W0423 03:48:41.639233 140308201023360 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/inference.py:543: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
            "W0423 03:48:46.509092 140308201023360 deprecation.py:323] From /content/automl/efficientdet/inference.py:543: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/graph_util_impl.py:359: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
            "W0423 03:48:46.509361 140308201023360 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/graph_util_impl.py:359: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
            "2020-04-23 03:48:49.252209: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "non-resource variables are not supported in the long term\n",
            "2020-04-23 03:48:51.357449: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F\n",
            "2020-04-23 03:48:51.362289: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2000170000 Hz\n",
            "2020-04-23 03:48:51.362470: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1e0b100 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 03:48:51.362501: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-04-23 03:48:51.364226: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-04-23 03:48:51.495340: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 03:48:51.496049: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1e0b2c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 03:48:51.496116: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2020-04-23 03:48:51.496306: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 03:48:51.496891: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2020-04-23 03:48:51.496942: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 03:48:51.498468: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-04-23 03:48:51.499972: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-04-23 03:48:51.500329: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-04-23 03:48:51.501751: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-04-23 03:48:51.502454: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-04-23 03:48:51.505209: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 03:48:51.505312: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 03:48:51.505846: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 03:48:51.506326: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n",
            "2020-04-23 03:48:51.506371: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 03:48:52.023053: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-04-23 03:48:52.023131: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 \n",
            "2020-04-23 03:48:52.023153: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N \n",
            "2020-04-23 03:48:52.023347: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 03:48:52.023906: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 03:48:52.024414: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-04-23 03:48:52.024456: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13970 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/inference.py:537: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.\n",
            "W0423 03:48:52.025150 139756903430016 deprecation.py:323] From /content/automl/efficientdet/inference.py:537: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.\n",
            "2020-04-23 03:48:57.258911: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 03:48:58.909640: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RW26DwfirQQN",
        "colab_type": "text"
      },
      "source": [
        "# 3. COCO evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cfn_tRFOWKMO"
      },
      "source": [
        "## 3.1 COCO evaluation on validation set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2s6E8IsVN0pB",
        "colab_type": "code",
        "outputId": "6799da8b-6637-4a11-97c8-18b1d22c6eb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "if 'val2017' not in os.listdir():\n",
        "  !wget http://images.cocodataset.org/zips/val2017.zip\n",
        "  !wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n",
        "  !unzip -q val2017.zip\n",
        "  !unzip annotations_trainval2017.zip\n",
        "\n",
        "  !mkdir tfrecrod\n",
        "  !PYTHONPATH=\".:$PYTHONPATH\"  python dataset/create_coco_tfrecord.py \\\n",
        "      --image_dir=val2017 \\\n",
        "      --caption_annotations_file=annotations/captions_val2017.json \\\n",
        "      --output_file_prefix=tfrecord/val \\\n",
        "      --num_shards=32"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-04-23 03:57:25--  http://images.cocodataset.org/zips/val2017.zip\n",
            "Resolving images.cocodataset.org (images.cocodataset.org)... 52.216.107.220\n",
            "Connecting to images.cocodataset.org (images.cocodataset.org)|52.216.107.220|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 815585330 (778M) [application/zip]\n",
            "Saving to: ‘val2017.zip’\n",
            "\n",
            "val2017.zip         100%[===================>] 777.80M  95.4MB/s    in 8.1s    \n",
            "\n",
            "2020-04-23 03:57:33 (95.6 MB/s) - ‘val2017.zip’ saved [815585330/815585330]\n",
            "\n",
            "--2020-04-23 03:57:34--  http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n",
            "Resolving images.cocodataset.org (images.cocodataset.org)... 52.216.244.76\n",
            "Connecting to images.cocodataset.org (images.cocodataset.org)|52.216.244.76|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 252907541 (241M) [application/zip]\n",
            "Saving to: ‘annotations_trainval2017.zip’\n",
            "\n",
            "annotations_trainva 100%[===================>] 241.19M  95.2MB/s    in 2.5s    \n",
            "\n",
            "2020-04-23 03:57:37 (95.2 MB/s) - ‘annotations_trainval2017.zip’ saved [252907541/252907541]\n",
            "\n",
            "Archive:  annotations_trainval2017.zip\n",
            "  inflating: annotations/instances_train2017.json  \n",
            "  inflating: annotations/instances_val2017.json  \n",
            "  inflating: annotations/captions_train2017.json  \n",
            "  inflating: annotations/captions_val2017.json  \n",
            "  inflating: annotations/person_keypoints_train2017.json  \n",
            "  inflating: annotations/person_keypoints_val2017.json  \n",
            "2020-04-23 03:58:08.456313: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "I0423 03:58:10.032314 140146012825472 create_coco_tfrecord.py:286] writing to output path: tfrecord/val\n",
            "I0423 03:58:10.115267 140146012825472 create_coco_tfrecord.py:238] Building caption index.\n",
            "I0423 03:58:10.120968 140146012825472 create_coco_tfrecord.py:250] 0 images are missing captions.\n",
            "I0423 03:58:10.176351 140146012825472 create_coco_tfrecord.py:324] On image 0 of 5000\n",
            "I0423 03:58:10.316092 140146012825472 create_coco_tfrecord.py:324] On image 100 of 5000\n",
            "I0423 03:58:10.428539 140146012825472 create_coco_tfrecord.py:324] On image 200 of 5000\n",
            "I0423 03:58:10.556268 140146012825472 create_coco_tfrecord.py:324] On image 300 of 5000\n",
            "I0423 03:58:10.671994 140146012825472 create_coco_tfrecord.py:324] On image 400 of 5000\n",
            "I0423 03:58:10.778093 140146012825472 create_coco_tfrecord.py:324] On image 500 of 5000\n",
            "I0423 03:58:10.964658 140146012825472 create_coco_tfrecord.py:324] On image 600 of 5000\n",
            "I0423 03:58:11.078955 140146012825472 create_coco_tfrecord.py:324] On image 700 of 5000\n",
            "I0423 03:58:11.213645 140146012825472 create_coco_tfrecord.py:324] On image 800 of 5000\n",
            "I0423 03:58:11.365745 140146012825472 create_coco_tfrecord.py:324] On image 900 of 5000\n",
            "I0423 03:58:11.494174 140146012825472 create_coco_tfrecord.py:324] On image 1000 of 5000\n",
            "I0423 03:58:11.630017 140146012825472 create_coco_tfrecord.py:324] On image 1100 of 5000\n",
            "I0423 03:58:11.754781 140146012825472 create_coco_tfrecord.py:324] On image 1200 of 5000\n",
            "I0423 03:58:11.859164 140146012825472 create_coco_tfrecord.py:324] On image 1300 of 5000\n",
            "I0423 03:58:12.024515 140146012825472 create_coco_tfrecord.py:324] On image 1400 of 5000\n",
            "I0423 03:58:12.154124 140146012825472 create_coco_tfrecord.py:324] On image 1500 of 5000\n",
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            "I0423 03:58:12.554709 140146012825472 create_coco_tfrecord.py:324] On image 1800 of 5000\n",
            "I0423 03:58:12.673296 140146012825472 create_coco_tfrecord.py:324] On image 1900 of 5000\n",
            "I0423 03:58:12.790292 140146012825472 create_coco_tfrecord.py:324] On image 2000 of 5000\n",
            "I0423 03:58:12.908614 140146012825472 create_coco_tfrecord.py:324] On image 2100 of 5000\n",
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            "I0423 03:58:13.337189 140146012825472 create_coco_tfrecord.py:324] On image 2300 of 5000\n",
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            "I0423 03:58:14.050753 140146012825472 create_coco_tfrecord.py:324] On image 2500 of 5000\n",
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            "I0423 03:58:14.915046 140146012825472 create_coco_tfrecord.py:324] On image 2700 of 5000\n",
            "I0423 03:58:15.451764 140146012825472 create_coco_tfrecord.py:324] On image 2800 of 5000\n",
            "I0423 03:58:16.098652 140146012825472 create_coco_tfrecord.py:324] On image 2900 of 5000\n",
            "I0423 03:58:16.388722 140146012825472 create_coco_tfrecord.py:324] On image 3000 of 5000\n",
            "I0423 03:58:16.540788 140146012825472 create_coco_tfrecord.py:324] On image 3100 of 5000\n",
            "I0423 03:58:16.809974 140146012825472 create_coco_tfrecord.py:324] On image 3200 of 5000\n",
            "I0423 03:58:17.186905 140146012825472 create_coco_tfrecord.py:324] On image 3300 of 5000\n",
            "I0423 03:58:17.576850 140146012825472 create_coco_tfrecord.py:324] On image 3400 of 5000\n",
            "I0423 03:58:17.963829 140146012825472 create_coco_tfrecord.py:324] On image 3500 of 5000\n",
            "I0423 03:58:18.373963 140146012825472 create_coco_tfrecord.py:324] On image 3600 of 5000\n",
            "I0423 03:58:18.919687 140146012825472 create_coco_tfrecord.py:324] On image 3700 of 5000\n",
            "I0423 03:58:19.495026 140146012825472 create_coco_tfrecord.py:324] On image 3800 of 5000\n",
            "I0423 03:58:20.152882 140146012825472 create_coco_tfrecord.py:324] On image 3900 of 5000\n",
            "I0423 03:58:20.634966 140146012825472 create_coco_tfrecord.py:324] On image 4000 of 5000\n",
            "I0423 03:58:21.046671 140146012825472 create_coco_tfrecord.py:324] On image 4100 of 5000\n",
            "I0423 03:58:21.530922 140146012825472 create_coco_tfrecord.py:324] On image 4200 of 5000\n",
            "I0423 03:58:21.874106 140146012825472 create_coco_tfrecord.py:324] On image 4300 of 5000\n",
            "I0423 03:58:21.912328 140146012825472 create_coco_tfrecord.py:324] On image 4400 of 5000\n",
            "I0423 03:58:22.016165 140146012825472 create_coco_tfrecord.py:324] On image 4500 of 5000\n",
            "I0423 03:58:22.191724 140146012825472 create_coco_tfrecord.py:324] On image 4600 of 5000\n",
            "I0423 03:58:22.484894 140146012825472 create_coco_tfrecord.py:324] On image 4700 of 5000\n",
            "I0423 03:58:22.865966 140146012825472 create_coco_tfrecord.py:324] On image 4800 of 5000\n",
            "I0423 03:58:23.320688 140146012825472 create_coco_tfrecord.py:324] On image 4900 of 5000\n",
            "I0423 03:58:23.890074 140146012825472 create_coco_tfrecord.py:336] Finished writing, skipped 0 annotations.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLHZUY3jQpZr",
        "colab_type": "code",
        "outputId": "6e8c30eb-3d16-4985-c5bb-b44d9e385c21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Evalute on validation set (takes about 10 mins for efficientdet-d0)\n",
        "!python main.py --mode=eval  \\\n",
        "    --model_name={MODEL}  --model_dir={ckpt_path}  \\\n",
        "    --validation_file_pattern=tfrecord/val*  \\\n",
        "    --val_json_file=annotations/instances_val2017.json  \\\n",
        "    --hparams=\"use_bfloat16=false\" --use_tpu=False"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-04-23 04:00:36.653339: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "non-resource variables are not supported in the long term\n",
            "WARNING:tensorflow:From main.py:222: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead.\n",
            "\n",
            "W0423 04:00:38.994576 140227058120576 module_wrapper.py:138] From main.py:222: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead.\n",
            "\n",
            "WARNING:tensorflow:From main.py:227: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead.\n",
            "\n",
            "W0423 04:00:38.994807 140227058120576 module_wrapper.py:138] From main.py:227: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead.\n",
            "\n",
            "WARNING:tensorflow:From main.py:230: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead.\n",
            "\n",
            "W0423 04:00:38.995015 140227058120576 module_wrapper.py:138] From main.py:230: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead.\n",
            "\n",
            "I0423 04:00:38.995242 140227058120576 main.py:242] {'name': 'efficientdet-d0', 'act_type': 'swish', 'image_size': 512, 'input_rand_hflip': True, 'train_scale_min': 0.1, 'train_scale_max': 2.0, 'autoaugment_policy': None, 'num_classes': 90, 'skip_crowd_during_training': True, 'min_level': 3, 'max_level': 7, 'num_scales': 3, 'aspect_ratios': [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)], 'anchor_scale': 4.0, 'is_training_bn': True, 'momentum': 0.9, 'learning_rate': 0.08, 'lr_warmup_init': 0.008, 'lr_warmup_epoch': 1.0, 'first_lr_drop_epoch': 200.0, 'second_lr_drop_epoch': 250.0, 'poly_lr_power': 0.9, 'clip_gradients_norm': 10.0, 'num_epochs': 15, 'data_format': 'channels_last', 'alpha': 0.25, 'gamma': 1.5, 'delta': 0.1, 'box_loss_weight': 50.0, 'weight_decay': 4e-05, 'use_bfloat16': False, 'use_tpu': False, 'box_class_repeats': 3, 'fpn_cell_repeats': 3, 'fpn_num_filters': 64, 'separable_conv': True, 'apply_bn_for_resampling': True, 'conv_after_downsample': False, 'conv_bn_act_pattern': False, 'use_native_resize_op': False, 'pooling_type': None, 'fpn_name': None, 'fpn_weight_method': None, 'fpn_config': None, 'survival_prob': None, 'lr_decay_method': 'cosine', 'moving_average_decay': 0.9998, 'ckpt_var_scope': None, 'var_exclude_expr': '.*/class-predict/.*', 'backbone_name': 'efficientnet-b0', 'backbone_config': None, 'resnet_depth': 50, 'model_name': 'efficientdet-d0', 'iterations_per_loop': 100, 'model_dir': '/content/automl/efficientdet/efficientdet-d0', 'num_shards': 8, 'num_examples_per_epoch': 120000, 'backbone_ckpt': '', 'ckpt': None, 'val_json_file': 'annotations/instances_val2017.json', 'testdev_dir': None, 'mode': 'eval'}\n",
            "WARNING:tensorflow:From main.py:294: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.\n",
            "\n",
            "W0423 04:00:38.995427 140227058120576 module_wrapper.py:138] From main.py:294: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.\n",
            "\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/content/automl/efficientdet/efficientdet-d0', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_tpu_config': TPUConfig(iterations_per_loop=100, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1), '_cluster': None}\n",
            "I0423 04:00:38.995901 140227058120576 estimator.py:191] Using config: {'_model_dir': '/content/automl/efficientdet/efficientdet-d0', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_tpu_config': TPUConfig(iterations_per_loop=100, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1), '_cluster': None}\n",
            "INFO:tensorflow:_TPUContext: eval_on_tpu True\n",
            "I0423 04:00:38.996996 140227058120576 tpu_context.py:216] _TPUContext: eval_on_tpu True\n",
            "WARNING:tensorflow:eval_on_tpu ignored because use_tpu is False.\n",
            "W0423 04:00:38.997503 140227058120576 tpu_context.py:218] eval_on_tpu ignored because use_tpu is False.\n",
            "INFO:tensorflow:Waiting for new checkpoint at /content/automl/efficientdet/efficientdet-d0\n",
            "I0423 04:00:38.997638 140227058120576 checkpoint_utils.py:125] Waiting for new checkpoint at /content/automl/efficientdet/efficientdet-d0\n",
            "INFO:tensorflow:Found new checkpoint at /content/automl/efficientdet/efficientdet-d0/model\n",
            "I0423 04:00:38.999212 140227058120576 checkpoint_utils.py:134] Found new checkpoint at /content/automl/efficientdet/efficientdet-d0/model\n",
            "I0423 04:00:38.999358 140227058120576 main.py:314] Starting to evaluate.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "W0423 04:00:39.005780 140227058120576 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/dataloader.py:330: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.\n",
            "W0423 04:00:39.025108 140227058120576 deprecation.py:323] From /content/automl/efficientdet/dataloader.py:330: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.\n",
            "2020-04-23 04:00:40.215372: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-04-23 04:00:40.251591: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:00:40.252156: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2020-04-23 04:00:40.252192: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 04:00:40.253585: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-04-23 04:00:40.255148: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-04-23 04:00:40.255467: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-04-23 04:00:40.256858: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-04-23 04:00:40.257532: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-04-23 04:00:40.260652: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 04:00:40.260790: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:00:40.261389: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:00:40.262022: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/dataloader.py:105: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0423 04:00:40.716829 140227058120576 deprecation.py:323] From /content/automl/efficientdet/dataloader.py:105: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/dataloader.py:108: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0423 04:00:40.721686 140227058120576 deprecation.py:323] From /content/automl/efficientdet/dataloader.py:108: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "I0423 04:00:44.863856 140227058120576 estimator.py:1169] Calling model_fn.\n",
            "INFO:tensorflow:Running eval on CPU/GPU\n",
            "I0423 04:00:44.864409 140227058120576 tpu_estimator.py:3171] Running eval on CPU/GPU\n",
            "I0423 04:00:44.865186 140227058120576 efficientdet_arch.py:679] {\n",
            "    \"name\": \"efficientdet-d0\",\n",
            "    \"act_type\": \"swish\",\n",
            "    \"image_size\": 512,\n",
            "    \"input_rand_hflip\": false,\n",
            "    \"train_scale_min\": 0.1,\n",
            "    \"train_scale_max\": 2.0,\n",
            "    \"autoaugment_policy\": null,\n",
            "    \"num_classes\": 90,\n",
            "    \"skip_crowd_during_training\": true,\n",
            "    \"min_level\": 3,\n",
            "    \"max_level\": 7,\n",
            "    \"num_scales\": 3,\n",
            "    \"aspect_ratios\": [\n",
            "        [\n",
            "            1.0,\n",
            "            1.0\n",
            "        ],\n",
            "        [\n",
            "            1.4,\n",
            "            0.7\n",
            "        ],\n",
            "        [\n",
            "            0.7,\n",
            "            1.4\n",
            "        ]\n",
            "    ],\n",
            "    \"anchor_scale\": 4.0,\n",
            "    \"is_training_bn\": false,\n",
            "    \"momentum\": 0.9,\n",
            "    \"learning_rate\": 0.08,\n",
            "    \"lr_warmup_init\": 0.008,\n",
            "    \"lr_warmup_epoch\": 1.0,\n",
            "    \"first_lr_drop_epoch\": 200.0,\n",
            "    \"second_lr_drop_epoch\": 250.0,\n",
            "    \"poly_lr_power\": 0.9,\n",
            "    \"clip_gradients_norm\": 10.0,\n",
            "    \"num_epochs\": 15,\n",
            "    \"data_format\": \"channels_last\",\n",
            "    \"alpha\": 0.25,\n",
            "    \"gamma\": 1.5,\n",
            "    \"delta\": 0.1,\n",
            "    \"box_loss_weight\": 50.0,\n",
            "    \"weight_decay\": 4e-05,\n",
            "    \"use_bfloat16\": false,\n",
            "    \"use_tpu\": false,\n",
            "    \"box_class_repeats\": 3,\n",
            "    \"fpn_cell_repeats\": 3,\n",
            "    \"fpn_num_filters\": 64,\n",
            "    \"separable_conv\": true,\n",
            "    \"apply_bn_for_resampling\": true,\n",
            "    \"conv_after_downsample\": false,\n",
            "    \"conv_bn_act_pattern\": false,\n",
            "    \"use_native_resize_op\": false,\n",
            "    \"pooling_type\": null,\n",
            "    \"fpn_name\": null,\n",
            "    \"fpn_weight_method\": null,\n",
            "    \"fpn_config\": null,\n",
            "    \"survival_prob\": null,\n",
            "    \"lr_decay_method\": \"cosine\",\n",
            "    \"moving_average_decay\": 0.9998,\n",
            "    \"ckpt_var_scope\": null,\n",
            "    \"var_exclude_expr\": \".*/class-predict/.*\",\n",
            "    \"backbone_name\": \"efficientnet-b0\",\n",
            "    \"backbone_config\": null,\n",
            "    \"resnet_depth\": 50,\n",
            "    \"model_name\": \"efficientdet-d0\",\n",
            "    \"iterations_per_loop\": 100,\n",
            "    \"model_dir\": \"/content/automl/efficientdet/efficientdet-d0\",\n",
            "    \"num_shards\": 8,\n",
            "    \"num_examples_per_epoch\": 120000,\n",
            "    \"backbone_ckpt\": \"\",\n",
            "    \"ckpt\": null,\n",
            "    \"val_json_file\": \"annotations/instances_val2017.json\",\n",
            "    \"testdev_dir\": null,\n",
            "    \"mode\": \"eval\",\n",
            "    \"batch_size\": 1\n",
            "}\n",
            "I0423 04:00:44.866083 140227058120576 efficientnet_builder.py:224] global_params= GlobalParams(batch_norm_momentum=0.99, batch_norm_epsilon=0.001, dropout_rate=0.2, data_format='channels_last', num_classes=1000, width_coefficient=1.0, depth_coefficient=1.0, depth_divisor=8, min_depth=None, survival_prob=0.0, relu_fn=<tensorflow.python.ops.custom_gradient.Bind object at 0x7f88e426a470>, batch_norm=<class 'utils.BatchNormalization'>, use_se=True, local_pooling=None, condconv_num_experts=None, clip_projection_output=False, blocks_args=['r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25', 'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25', 'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25', 'r1_k3_s11_e6_i192_o320_se0.25'], fix_head_stem=None)\n",
            "I0423 04:00:44.883214 140227058120576 efficientnet_model.py:151] round_filter input=32 output=32\n",
            "I0423 04:00:44.891434 140227058120576 efficientnet_model.py:151] round_filter input=32 output=32\n",
            "I0423 04:00:44.891571 140227058120576 efficientnet_model.py:151] round_filter input=16 output=16\n",
            "I0423 04:00:44.917921 140227058120576 efficientnet_model.py:151] round_filter input=16 output=16\n",
            "I0423 04:00:44.918057 140227058120576 efficientnet_model.py:151] round_filter input=24 output=24\n",
            "I0423 04:00:44.969600 140227058120576 efficientnet_model.py:151] round_filter input=24 output=24\n",
            "I0423 04:00:44.969725 140227058120576 efficientnet_model.py:151] round_filter input=40 output=40\n",
            "I0423 04:00:45.021621 140227058120576 efficientnet_model.py:151] round_filter input=40 output=40\n",
            "I0423 04:00:45.021754 140227058120576 efficientnet_model.py:151] round_filter input=80 output=80\n",
            "I0423 04:00:45.097679 140227058120576 efficientnet_model.py:151] round_filter input=80 output=80\n",
            "I0423 04:00:45.097793 140227058120576 efficientnet_model.py:151] round_filter input=112 output=112\n",
            "I0423 04:00:45.179541 140227058120576 efficientnet_model.py:151] round_filter input=112 output=112\n",
            "I0423 04:00:45.179678 140227058120576 efficientnet_model.py:151] round_filter input=192 output=192\n",
            "I0423 04:00:45.284213 140227058120576 efficientnet_model.py:151] round_filter input=192 output=192\n",
            "I0423 04:00:45.284355 140227058120576 efficientnet_model.py:151] round_filter input=320 output=320\n",
            "I0423 04:00:45.313869 140227058120576 efficientnet_model.py:151] round_filter input=1280 output=1280\n",
            "I0423 04:00:47.958207 140227058120576 api.py:587] Built stem layers with output shape: (1, 256, 256, 32)\n",
            "I0423 04:00:50.547974 140227058120576 api.py:587] Block input: efficientnet-b0/model/stem/IdentityN:0 shape: (1, 256, 256, 32)\n",
            "I0423 04:00:50.556908 140227058120576 api.py:587] Block input depth: 32 output depth: 16\n",
            "I0423 04:00:50.590815 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_0/IdentityN:0 shape: (1, 256, 256, 32)\n",
            "I0423 04:00:50.755205 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 32)\n",
            "I0423 04:00:50.787436 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_0/Identity_1:0 shape: (1, 256, 256, 16)\n",
            "I0423 04:00:50.836828 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_0/Identity_1:0 shape: (1, 256, 256, 16)\n",
            "I0423 04:00:50.845409 140227058120576 api.py:587] Block input depth: 16 output depth: 24\n",
            "I0423 04:00:50.883781 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_1/IdentityN:0 shape: (1, 256, 256, 96)\n",
            "I0423 04:00:50.916726 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_1/IdentityN_1:0 shape: (1, 128, 128, 96)\n",
            "I0423 04:00:50.956085 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 96)\n",
            "I0423 04:00:50.993716 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_1/Identity_2:0 shape: (1, 128, 128, 24)\n",
            "I0423 04:00:51.006580 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_1/Identity_2:0 shape: (1, 128, 128, 24)\n",
            "I0423 04:00:51.015420 140227058120576 api.py:587] Block input depth: 24 output depth: 24\n",
            "I0423 04:00:51.049110 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_2/IdentityN:0 shape: (1, 128, 128, 144)\n",
            "I0423 04:00:51.086016 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_2/IdentityN_1:0 shape: (1, 128, 128, 144)\n",
            "I0423 04:00:51.124205 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 144)\n",
            "I0423 04:00:51.156827 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_2/Add:0 shape: (1, 128, 128, 24)\n",
            "I0423 04:00:51.167029 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_2/Add:0 shape: (1, 128, 128, 24)\n",
            "I0423 04:00:51.175647 140227058120576 api.py:587] Block input depth: 24 output depth: 40\n",
            "I0423 04:00:51.211880 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_3/IdentityN:0 shape: (1, 128, 128, 144)\n",
            "I0423 04:00:51.245679 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_3/IdentityN_1:0 shape: (1, 64, 64, 144)\n",
            "I0423 04:00:51.286578 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 144)\n",
            "I0423 04:00:51.325401 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_3/Identity_2:0 shape: (1, 64, 64, 40)\n",
            "I0423 04:00:51.335214 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_3/Identity_2:0 shape: (1, 64, 64, 40)\n",
            "I0423 04:00:51.343787 140227058120576 api.py:587] Block input depth: 40 output depth: 40\n",
            "I0423 04:00:51.376570 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_4/IdentityN:0 shape: (1, 64, 64, 240)\n",
            "I0423 04:00:51.411386 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_4/IdentityN_1:0 shape: (1, 64, 64, 240)\n",
            "I0423 04:00:51.446691 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 240)\n",
            "I0423 04:00:51.481863 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_4/Add:0 shape: (1, 64, 64, 40)\n",
            "I0423 04:00:51.492512 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_4/Add:0 shape: (1, 64, 64, 40)\n",
            "I0423 04:00:51.504062 140227058120576 api.py:587] Block input depth: 40 output depth: 80\n",
            "I0423 04:00:51.538872 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_5/IdentityN:0 shape: (1, 64, 64, 240)\n",
            "I0423 04:00:51.572095 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_5/IdentityN_1:0 shape: (1, 32, 32, 240)\n",
            "I0423 04:00:51.609448 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 240)\n",
            "I0423 04:00:51.641531 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_5/Identity_2:0 shape: (1, 32, 32, 80)\n",
            "I0423 04:00:51.651318 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_5/Identity_2:0 shape: (1, 32, 32, 80)\n",
            "I0423 04:00:51.663240 140227058120576 api.py:587] Block input depth: 80 output depth: 80\n",
            "I0423 04:00:51.700784 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_6/IdentityN:0 shape: (1, 32, 32, 480)\n",
            "I0423 04:00:51.734607 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_6/IdentityN_1:0 shape: (1, 32, 32, 480)\n",
            "I0423 04:00:51.770525 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 480)\n",
            "I0423 04:00:51.805452 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_6/Add:0 shape: (1, 32, 32, 80)\n",
            "I0423 04:00:51.815392 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_6/Add:0 shape: (1, 32, 32, 80)\n",
            "I0423 04:00:51.823784 140227058120576 api.py:587] Block input depth: 80 output depth: 80\n",
            "I0423 04:00:51.857498 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_7/IdentityN:0 shape: (1, 32, 32, 480)\n",
            "I0423 04:00:51.891165 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_7/IdentityN_1:0 shape: (1, 32, 32, 480)\n",
            "I0423 04:00:51.928906 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 480)\n",
            "I0423 04:00:51.968694 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_7/Add:0 shape: (1, 32, 32, 80)\n",
            "I0423 04:00:51.980522 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_7/Add:0 shape: (1, 32, 32, 80)\n",
            "I0423 04:00:51.992969 140227058120576 api.py:587] Block input depth: 80 output depth: 112\n",
            "I0423 04:00:52.029768 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_8/IdentityN:0 shape: (1, 32, 32, 480)\n",
            "I0423 04:00:52.064205 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_8/IdentityN_1:0 shape: (1, 32, 32, 480)\n",
            "I0423 04:00:52.100060 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 480)\n",
            "I0423 04:00:52.134609 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_8/Identity_2:0 shape: (1, 32, 32, 112)\n",
            "I0423 04:00:52.144496 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_8/Identity_2:0 shape: (1, 32, 32, 112)\n",
            "I0423 04:00:52.153087 140227058120576 api.py:587] Block input depth: 112 output depth: 112\n",
            "I0423 04:00:52.190446 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_9/IdentityN:0 shape: (1, 32, 32, 672)\n",
            "I0423 04:00:52.229624 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_9/IdentityN_1:0 shape: (1, 32, 32, 672)\n",
            "I0423 04:00:52.356455 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 672)\n",
            "I0423 04:00:52.395300 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_9/Add:0 shape: (1, 32, 32, 112)\n",
            "I0423 04:00:52.407405 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_9/Add:0 shape: (1, 32, 32, 112)\n",
            "I0423 04:00:52.415957 140227058120576 api.py:587] Block input depth: 112 output depth: 112\n",
            "I0423 04:00:52.451425 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_10/IdentityN:0 shape: (1, 32, 32, 672)\n",
            "I0423 04:00:52.485975 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_10/IdentityN_1:0 shape: (1, 32, 32, 672)\n",
            "I0423 04:00:52.522543 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 672)\n",
            "I0423 04:00:52.560052 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_10/Add:0 shape: (1, 32, 32, 112)\n",
            "I0423 04:00:52.570708 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_10/Add:0 shape: (1, 32, 32, 112)\n",
            "I0423 04:00:52.579447 140227058120576 api.py:587] Block input depth: 112 output depth: 192\n",
            "I0423 04:00:52.615727 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_11/IdentityN:0 shape: (1, 32, 32, 672)\n",
            "I0423 04:00:52.649472 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_11/IdentityN_1:0 shape: (1, 16, 16, 672)\n",
            "I0423 04:00:52.689044 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 672)\n",
            "I0423 04:00:52.721709 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_11/Identity_2:0 shape: (1, 16, 16, 192)\n",
            "I0423 04:00:52.732023 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_11/Identity_2:0 shape: (1, 16, 16, 192)\n",
            "I0423 04:00:52.740523 140227058120576 api.py:587] Block input depth: 192 output depth: 192\n",
            "I0423 04:00:52.779492 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_12/IdentityN:0 shape: (1, 16, 16, 1152)\n",
            "I0423 04:00:52.818284 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_12/IdentityN_1:0 shape: (1, 16, 16, 1152)\n",
            "I0423 04:00:52.856240 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 1152)\n",
            "I0423 04:00:52.898282 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_12/Add:0 shape: (1, 16, 16, 192)\n",
            "I0423 04:00:52.910576 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_12/Add:0 shape: (1, 16, 16, 192)\n",
            "I0423 04:00:52.920511 140227058120576 api.py:587] Block input depth: 192 output depth: 192\n",
            "I0423 04:00:52.958303 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_13/IdentityN:0 shape: (1, 16, 16, 1152)\n",
            "I0423 04:00:53.000731 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_13/IdentityN_1:0 shape: (1, 16, 16, 1152)\n",
            "I0423 04:00:53.044622 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 1152)\n",
            "I0423 04:00:53.080706 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_13/Add:0 shape: (1, 16, 16, 192)\n",
            "I0423 04:00:53.090627 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_13/Add:0 shape: (1, 16, 16, 192)\n",
            "I0423 04:00:53.103000 140227058120576 api.py:587] Block input depth: 192 output depth: 192\n",
            "I0423 04:00:53.141762 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_14/IdentityN:0 shape: (1, 16, 16, 1152)\n",
            "I0423 04:00:53.180427 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_14/IdentityN_1:0 shape: (1, 16, 16, 1152)\n",
            "I0423 04:00:53.220879 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 1152)\n",
            "I0423 04:00:53.256151 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_14/Add:0 shape: (1, 16, 16, 192)\n",
            "I0423 04:00:53.266129 140227058120576 api.py:587] Block input: efficientnet-b0/model/blocks_14/Add:0 shape: (1, 16, 16, 192)\n",
            "I0423 04:00:53.277020 140227058120576 api.py:587] Block input depth: 192 output depth: 320\n",
            "I0423 04:00:53.320441 140227058120576 api.py:587] Expand: efficientnet-b0/model/blocks_15/IdentityN:0 shape: (1, 16, 16, 1152)\n",
            "I0423 04:00:53.360468 140227058120576 api.py:587] DWConv: efficientnet-b0/model/blocks_15/IdentityN_1:0 shape: (1, 16, 16, 1152)\n",
            "I0423 04:00:53.397530 140227058120576 api.py:587] Built Squeeze and Excitation with tensor shape: (1, 1, 1, 1152)\n",
            "I0423 04:00:53.433154 140227058120576 api.py:587] Project: efficientnet-b0/model/blocks_15/Identity_2:0 shape: (1, 16, 16, 320)\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "W0423 04:00:53.444358 140227058120576 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/internal/flops_registry.py:142: tensor_shape_from_node_def_name (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`\n",
            "Parsing Inputs...\n",
            "I0423 04:00:53.720528 140227058120576 efficientdet_arch.py:684] backbone params/flops = 3.595388M, 1.933562589B\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:121: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.Conv2D` instead.\n",
            "W0423 04:00:53.721009 140227058120576 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:121: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.Conv2D` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "W0423 04:00:53.724433 140227058120576 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:147: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.MaxPooling2D instead.\n",
            "W0423 04:00:53.759173 140227058120576 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:147: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.MaxPooling2D instead.\n",
            "I0423 04:00:53.766402 140227058120576 efficientdet_arch.py:472] building cell 0\n",
            "I0423 04:00:53.766763 140227058120576 efficientdet_arch.py:589] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/efficientdet_arch.py:646: separable_conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.SeparableConv2D` instead.\n",
            "W0423 04:00:53.779564 140227058120576 deprecation.py:323] From /content/automl/efficientdet/efficientdet_arch.py:646: separable_conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.keras.layers.SeparableConv2D` instead.\n",
            "I0423 04:00:53.816630 140227058120576 efficientdet_arch.py:589] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}\n",
            "I0423 04:00:53.903512 140227058120576 efficientdet_arch.py:589] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}\n",
            "I0423 04:00:53.983085 140227058120576 efficientdet_arch.py:589] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}\n",
            "I0423 04:00:54.068620 140227058120576 efficientdet_arch.py:589] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}\n",
            "I0423 04:00:54.157358 140227058120576 efficientdet_arch.py:589] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}\n",
            "I0423 04:00:54.240069 140227058120576 efficientdet_arch.py:589] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}\n",
            "I0423 04:00:54.303977 140227058120576 efficientdet_arch.py:589] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}\n",
            "I0423 04:00:54.360051 140227058120576 efficientdet_arch.py:472] building cell 1\n",
            "I0423 04:00:54.360411 140227058120576 efficientdet_arch.py:589] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}\n",
            "I0423 04:00:54.410348 140227058120576 efficientdet_arch.py:589] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}\n",
            "I0423 04:00:54.461001 140227058120576 efficientdet_arch.py:589] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}\n",
            "I0423 04:00:54.515681 140227058120576 efficientdet_arch.py:589] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}\n",
            "I0423 04:00:54.566288 140227058120576 efficientdet_arch.py:589] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}\n",
            "I0423 04:00:54.620745 140227058120576 efficientdet_arch.py:589] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}\n",
            "I0423 04:00:54.672744 140227058120576 efficientdet_arch.py:589] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}\n",
            "I0423 04:00:54.726210 140227058120576 efficientdet_arch.py:589] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}\n",
            "I0423 04:00:54.775903 140227058120576 efficientdet_arch.py:472] building cell 2\n",
            "I0423 04:00:54.776250 140227058120576 efficientdet_arch.py:589] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}\n",
            "I0423 04:00:54.828539 140227058120576 efficientdet_arch.py:589] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}\n",
            "I0423 04:00:54.879253 140227058120576 efficientdet_arch.py:589] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}\n",
            "I0423 04:00:54.940471 140227058120576 efficientdet_arch.py:589] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}\n",
            "I0423 04:00:55.006071 140227058120576 efficientdet_arch.py:589] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}\n",
            "I0423 04:00:55.065782 140227058120576 efficientdet_arch.py:589] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}\n",
            "I0423 04:00:55.123272 140227058120576 efficientdet_arch.py:589] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}\n",
            "I0423 04:00:55.177192 140227058120576 efficientdet_arch.py:589] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}\n",
            "Parsing Inputs...\n",
            "I0423 04:00:55.690232 140227058120576 efficientdet_arch.py:689] backbone+fpn params/flops = 3.791669M, 2.076854536B\n",
            "Parsing Inputs...\n",
            "I0423 04:00:57.179839 140227058120576 efficientdet_arch.py:694] backbone+fpn+box params/flops = 3.880067M, 2.535978424B\n",
            "I0423 04:00:57.180178 140227058120576 det_model_fn.py:95] LR schedule method: cosine\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/det_model_fn.py:623: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead.\n",
            "\n",
            "W0423 04:00:57.461426 140227058120576 module_wrapper.py:138] From /content/automl/efficientdet/det_model_fn.py:623: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead.\n",
            "\n",
            "I0423 04:00:57.487451 140227058120576 det_model_fn.py:553] Eval val with groudtruths annotations/instances_val2017.json.\n",
            "WARNING:tensorflow:From /content/automl/efficientdet/anchors.py:589: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "tf.py_func is deprecated in TF V2. Instead, there are two\n",
            "    options available in V2.\n",
            "    - tf.py_function takes a python function which manipulates tf eager\n",
            "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
            "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
            "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
            "    being differentiable using a gradient tape.\n",
            "    - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n",
            "    (it is not differentiable, and manipulates numpy arrays). It drops the\n",
            "    stateful argument making all functions stateful.\n",
            "    \n",
            "W0423 04:00:57.497913 140227058120576 deprecation.py:323] From /content/automl/efficientdet/anchors.py:589: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "tf.py_func is deprecated in TF V2. Instead, there are two\n",
            "    options available in V2.\n",
            "    - tf.py_function takes a python function which manipulates tf eager\n",
            "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
            "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
            "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
            "    being differentiable using a gradient tape.\n",
            "    - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n",
            "    (it is not differentiable, and manipulates numpy arrays). It drops the\n",
            "    stateful argument making all functions stateful.\n",
            "    \n",
            "loading annotations into memory...\n",
            "Done (t=0.64s)\n",
            "creating index...\n",
            "index created!\n",
            "I0423 04:00:58.181418 140227058120576 det_model_fn.py:616] Load EMA vars with ema_decay=0.999800\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "I0423 04:00:58.632483 140227058120576 estimator.py:1171] Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2020-04-23T04:00:58Z\n",
            "I0423 04:00:58.646218 140227058120576 evaluation.py:255] Starting evaluation at 2020-04-23T04:00:58Z\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "I0423 04:00:59.195226 140227058120576 monitored_session.py:246] Graph was finalized.\n",
            "2020-04-23 04:00:59.201078: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2300000000 Hz\n",
            "2020-04-23 04:00:59.201337: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x32a72c0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 04:00:59.201372: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-04-23 04:00:59.311892: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:00:59.312619: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x32a7100 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-23 04:00:59.312663: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2020-04-23 04:00:59.312929: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:00:59.313494: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2020-04-23 04:00:59.313542: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 04:00:59.313630: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-04-23 04:00:59.313654: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-04-23 04:00:59.313681: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-04-23 04:00:59.313701: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-04-23 04:00:59.313721: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-04-23 04:00:59.313743: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 04:00:59.313852: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:00:59.314516: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:00:59.315100: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n",
            "2020-04-23 04:00:59.315154: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-04-23 04:00:59.851948: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-04-23 04:00:59.852012: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 \n",
            "2020-04-23 04:00:59.852025: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N \n",
            "2020-04-23 04:00:59.852273: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:00:59.852933: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-23 04:00:59.853427: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-04-23 04:00:59.853471: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13970 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "INFO:tensorflow:Restoring parameters from /content/automl/efficientdet/efficientdet-d0/model\n",
            "I0423 04:00:59.854976 140227058120576 saver.py:1293] Restoring parameters from /content/automl/efficientdet/efficientdet-d0/model\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "I0423 04:01:01.663889 140227058120576 session_manager.py:505] Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "I0423 04:01:01.758667 140227058120576 session_manager.py:508] Done running local_init_op.\n",
            "2020-04-23 04:01:05.208335: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-23 04:01:07.105233: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "INFO:tensorflow:Evaluation [500/5000]\n",
            "I0423 04:01:57.408222 140227058120576 evaluation.py:167] Evaluation [500/5000]\n",
            "INFO:tensorflow:Evaluation [1000/5000]\n",
            "I0423 04:02:47.382441 140227058120576 evaluation.py:167] Evaluation [1000/5000]\n",
            "INFO:tensorflow:Evaluation [1500/5000]\n",
            "I0423 04:03:38.382599 140227058120576 evaluation.py:167] Evaluation [1500/5000]\n",
            "INFO:tensorflow:Evaluation [2000/5000]\n",
            "I0423 04:04:28.952319 140227058120576 evaluation.py:167] Evaluation [2000/5000]\n",
            "INFO:tensorflow:Evaluation [2500/5000]\n",
            "I0423 04:05:18.460070 140227058120576 evaluation.py:167] Evaluation [2500/5000]\n",
            "INFO:tensorflow:Evaluation [3000/5000]\n",
            "I0423 04:06:09.812344 140227058120576 evaluation.py:167] Evaluation [3000/5000]\n",
            "INFO:tensorflow:Evaluation [3500/5000]\n",
            "I0423 04:07:00.327199 140227058120576 evaluation.py:167] Evaluation [3500/5000]\n",
            "INFO:tensorflow:Evaluation [4000/5000]\n",
            "I0423 04:07:50.087413 140227058120576 evaluation.py:167] Evaluation [4000/5000]\n",
            "INFO:tensorflow:Evaluation [4500/5000]\n",
            "I0423 04:08:39.949222 140227058120576 evaluation.py:167] Evaluation [4500/5000]\n",
            "INFO:tensorflow:Evaluation [5000/5000]\n",
            "I0423 04:09:29.806091 140227058120576 evaluation.py:167] Evaluation [5000/5000]\n",
            "Loading and preparing results...\n",
            "Converting ndarray to lists...\n",
            "(500000, 7)\n",
            "0/500000\n",
            "DONE (t=3.38s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=70.58s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=10.69s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.335\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.515\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.353\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.125\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.388\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.526\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.287\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.439\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.465\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.193\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.549\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.685\n",
            "INFO:tensorflow:Inference Time : 597.93359s\n",
            "I0423 04:10:56.580007 140227058120576 evaluation.py:273] Inference Time : 597.93359s\n",
            "INFO:tensorflow:Finished evaluation at 2020-04-23-04:10:56\n",
            "I0423 04:10:56.580248 140227058120576 evaluation.py:276] Finished evaluation at 2020-04-23-04:10:56\n",
            "INFO:tensorflow:Saving dict for global step 0: AP = 0.33491418, AP50 = 0.5154238, AP75 = 0.35295796, APl = 0.52646726, APm = 0.38791642, APs = 0.12475659, ARl = 0.6851952, ARm = 0.5492734, ARmax1 = 0.2873492, ARmax10 = 0.43856248, ARmax100 = 0.46534252, ARs = 0.19252816, box_loss = 0.0, cls_loss = 30.711409, global_step = 0, loss = 30.806612\n",
            "I0423 04:10:56.580455 140227058120576 estimator.py:2066] Saving dict for global step 0: AP = 0.33491418, AP50 = 0.5154238, AP75 = 0.35295796, APl = 0.52646726, APm = 0.38791642, APs = 0.12475659, ARl = 0.6851952, ARm = 0.5492734, ARmax1 = 0.2873492, ARmax10 = 0.43856248, ARmax100 = 0.46534252, ARs = 0.19252816, box_loss = 0.0, cls_loss = 30.711409, global_step = 0, loss = 30.806612\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 0: /content/automl/efficientdet/efficientdet-d0/model\n",
            "I0423 04:10:57.555701 140227058120576 estimator.py:2127] Saving 'checkpoint_path' summary for global step 0: /content/automl/efficientdet/efficientdet-d0/model\n",
            "INFO:tensorflow:evaluation_loop marked as finished\n",
            "I0423 04:10:57.556593 140227058120576 error_handling.py:115] evaluation_loop marked as finished\n",
            "I0423 04:10:57.556794 140227058120576 main.py:320] Eval results: {'AP': 0.33491418, 'AP50': 0.5154238, 'AP75': 0.35295796, 'APl': 0.52646726, 'APm': 0.38791642, 'APs': 0.12475659, 'ARl': 0.6851952, 'ARm': 0.5492734, 'ARmax1': 0.2873492, 'ARmax10': 0.43856248, 'ARmax100': 0.46534252, 'ARs': 0.19252816, 'box_loss': 0.0, 'cls_loss': 30.711409, 'loss': 30.806612, 'global_step': 0}\n",
            "I0423 04:10:57.556905 140227058120576 main.py:326] /content/automl/efficientdet/efficientdet-d0/model has no global step info: stop!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mDp_acD1pUcx",
        "colab_type": "text"
      },
      "source": [
        "## 3.2 COCO evaluation on test-dev."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RI_dvx5pbBK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Eval on test-dev is slow (~40 mins), please be cautious. \n",
        "RUN_EXPENSIVE_TEST_DEV_EVAL = True  #@param\n",
        "\n",
        "if RUN_EXPENSIVE_TEST_DEV_EVAL == True:\n",
        "  !rm *.zip *.tar tfrecord/ val2017/   # Cleanup disk space\n",
        "  # Download and convert test-dev data.\n",
        "  if \"test2017\" not in os.listdir():\n",
        "    !wget http://images.cocodataset.org/zips/test2017.zip\n",
        "    !unzip -q test2017.zip\n",
        "    !wget http://images.cocodataset.org/annotations/image_info_test2017.zip\n",
        "    !unzip image_info_test2017.zip\n",
        "\n",
        "    !mkdir tfrecrod\n",
        "    !PYTHONPATH=\".:$PYTHONPATH\"  python dataset/create_coco_tfrecord.py \\\n",
        "          --image_dir=test2017 \\\n",
        "          --image_info_file=annotations/image_info_test-dev2017.json \\\n",
        "          --output_file_prefix=tfrecord/testdev \\\n",
        "          --num_shards=32\n",
        "\n",
        "  # Evalute on validation set: non-empty testdev_dir is the key pararmeter.\n",
        "  # Also, test-dev has 20288 images rather than val 5000 images.\n",
        "  !mkdir testdev_output\n",
        "  !python main.py --mode=eval  \\\n",
        "      --model_name={MODEL}  --model_dir={ckpt_path}  \\\n",
        "      --validation_file_pattern=tfrecord/testdev*  \\\n",
        "      --hparams=\"use_bfloat16=false\" --use_tpu=False --eval_batch_size=8 \\\n",
        "      --testdev_dir='testdev_output' --eval_samples=20288\n",
        "  !rm -rf test2017  # delete images to release disk space.\n",
        "  # Now you can submit testdev_output/detections_test-dev2017_test_results.json to\n",
        "  # coco server: https://competitions.codalab.org/competitions/20794#participate"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RW90fiMiyg4n",
        "colab_type": "text"
      },
      "source": [
        "# 4. Training EfficientDets on PASCAL."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C98Ye0MEyuKD",
        "colab_type": "text"
      },
      "source": [
        "## 4.1 Prepare data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6PC6QrMlylOF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get pascal voc 2012 trainval data\n",
        "import os\n",
        "if 'VOCdevkit' not in os.listdir():\n",
        "  !wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar\n",
        "  !tar xf VOCtrainval_11-May-2012.tar\n",
        "\n",
        "  !mkdir tfrecord\n",
        "  !PYTHONPATH=\".:$PYTHONPATH\"  python dataset/create_pascal_tfrecord.py  \\\n",
        "    --data_dir=VOCdevkit --year=VOC2012  --output_path=tfrecord/pascal\n",
        "\n",
        "# Pascal has 5717 train images with 100 shards epoch, here we use a single shard\n",
        "# for demo, but users should use all shards pascal-*-of-00100.tfrecord.\n",
        "file_pattern = 'pascal-00000-of-00100.tfrecord'  # @param\n",
        "images_per_epoch = 57 * len(tf.io.gfile.glob('tfrecord/' + file_pattern))\n",
        "images_per_epoch = images_per_epoch // 8 * 8  # round to 64.\n",
        "print('images_per_epoch = {}'.format(images_per_epoch))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZcxDDCCW0ndv",
        "colab_type": "text"
      },
      "source": [
        "## 4.2 Train Pascal VOC 2012 from ImageNet checkpoint for Backbone."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHPgm9Q13X-l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train efficientdet from scratch with backbone checkpoint.\n",
        "backbone_name = {\n",
        "    'efficientdet-d0': 'efficientnet-b0',\n",
        "    'efficientdet-d1': 'efficientnet-b1',\n",
        "    'efficientdet-d2': 'efficientnet-b2',\n",
        "    'efficientdet-d3': 'efficientnet-b3',\n",
        "    'efficientdet-d4': 'efficientnet-b4',\n",
        "    'efficientdet-d5': 'efficientnet-b5',\n",
        "    'efficientdet-d6': 'efficientnet-b6',\n",
        "    'efficientdet-d7': 'efficientnet-b6',\n",
        "}[MODEL]\n",
        "\n",
        "\n",
        "# generating train tfrecord is large, so we skip the execution here.\n",
        "import os\n",
        "if backbone_name not in os.listdir():\n",
        "  !wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/{backbone_name}.tar.gz\n",
        "  !tar xf {backbone_name}.tar.gz\n",
        "\n",
        "!mkdir /tmp/model_dir\n",
        "# key option: use --backbone_ckpt rather than --ckpt.\n",
        "# Don't use ema since we only train a few steps.\n",
        "!python main.py --mode=train_and_eval \\\n",
        "    --training_file_pattern=tfrecord/{file_pattern} \\\n",
        "    --validation_file_pattern=tfrecord/{file_pattern} \\\n",
        "    --val_json_file=tfrecord/json_pascal.json \\\n",
        "    --model_name={MODEL} \\\n",
        "    --model_dir=/tmp/model_dir/{MODEL}-scratch  \\\n",
        "    --backbone_ckpt={backbone_name} \\\n",
        "    --train_batch_size=8 \\\n",
        "    --eval_batch_size=8 --eval_samples={images_per_epoch}  \\\n",
        "    --num_examples_per_epoch={images_per_epoch}  --num_epochs=1  \\\n",
        "    --hparams=\"use_bfloat16=false,num_classes=20,moving_average_decay=0\" \\\n",
        "    --use_tpu=False "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SKHu-3lBwTiM",
        "colab_type": "text"
      },
      "source": [
        "## 4.3 Train Pascal VOC 2012 from COCO checkpoint for the whole net."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SD59rsZJc1WW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# generating train tfrecord is large, so we skip the execution here.\n",
        "import os\n",
        "if MODEL not in os.listdir():\n",
        "  !wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/{MODEL}.tar.gz\n",
        "  !tar xf {MODEL}.tar.gz\n",
        "\n",
        "!mkdir /tmp/model_dir/\n",
        "# key option: use --ckpt rather than --backbone_ckpt.\n",
        "!python main.py --mode=train_and_eval \\\n",
        "    --training_file_pattern=tfrecord/{file_pattern} \\\n",
        "    --validation_file_pattern=tfrecord/{file_pattern} \\\n",
        "    --val_json_file=tfrecord/json_pascal.json \\\n",
        "    --model_name={MODEL} \\\n",
        "    --model_dir=/tmp/model_dir/{MODEL}-finetune \\\n",
        "    --ckpt={MODEL} \\\n",
        "    --train_batch_size=8 \\\n",
        "    --eval_batch_size=8 --eval_samples={images_per_epoch}  \\\n",
        "    --num_examples_per_epoch={images_per_epoch}  --num_epochs=1  \\\n",
        "    --hparams=\"use_bfloat16=false,num_classes=20,moving_average_decay=0\" \\\n",
        "    --use_tpu=False "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QcBGPMCXRC8q",
        "colab_type": "text"
      },
      "source": [
        "## 4.4 View tensorboard for loss and accuracy.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vrkty06SRD0k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%load_ext tensorboard\n",
        "%tensorboard --logdir /tmp/model_dir/\n",
        "# Notably, this is just a demo with almost zero accuracy due to very limited\n",
        "# training steps, but we can see finetuning has smaller loss than training\n",
        "# from scratch at the begining."
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}